What’s up with student’s TikTok FYPs?
Isabella Ranum 2287948222
2025-05-12
I wanted to explore the type of content student’s are recommended on their TikTok FYP (For You Pages) and how indicative they believe it is of their interests!
First, let me explain the data collected!
I collected data from students on the following variables: age, how long they’ve used TikTok, the amount of hours they spend scrolling their FYP each day, the types of videos that have featured regularly on their FYPs this month, the types of videos on their FYP that they have interacted with (likes, shares etc) during the month, how accurate they believe their TikTok FYP is of their interests and if they think the TikTok FYP is better tailored to their interests than other social media homepages.
I was interested in seeing whether there was a relationship between some of these variables as in my mind, I thought the longer a student spent scrolling on TikTok, the better tailored they probably find it to their interests!
A little more information about the survey
Data was collected from students on two dates; 31 March 2025 and 11 May 2025 via a Google Form. I then used the timestamp for my first plot!
Plot 1: Top 5 video categories students interacted with on the TikTok FYP grouped by time scrolling
I decided to first create a bar chart showing the top 5 video categories students interacted with on the TikTok For You Page (FYP), grouped by time spent scrolling (per day). I grouped the time spent scrolling into small, medium and high.
I used the functions rename(), mutate(), groupby(), summarise(), and arrange() to manipulate my data. After, I used geom_col() to create the bar charts and coord_flip() to display the most interacted with video categories in descending order. The heights of the bars height of the bars represent the count of interactions!
I was interested in seeing what types of videos students who spent little time scrolling the TikTok FYP were interacting with via likes, comments, shares etc vs what types of videos students who were spending a high amount of time scrolling were interacting with, and if there were any differences. Now, I found when I collated this data I should have only allowed one category to be selected. As you can see, some of the top categories for each scrolling group were actually a combination of video types, not just one e.g. beauty.
Further analysing my plot…
What I found through my results was that overall, students with a medium - high amount of time spent scrolling interacted most with meme humour / comedy. For an App with a FYP algorith designed to keep people engaged and continually scrolling, it does appear to make sense comedy videos would be a key way to achieve higher scrolling time.
Interestingly, the highest video type interacted with by students who had a low scrolling time on TikTok was storytimes / advice! This was a little confusing to me as advice videos normally have a higher run time, however maybe these students are only watching a couple before quitting the app.
Plot 2: Is the TikTok FYP better tailored to students’ interests than other social media homepages, grouped by hours spent scrolling
This bring us to my second plot!
I added another question to my survey prior to the results on May 11th, as I was interested in finding out if TikTok’s supposed “better than the rest” personal algorithm for it’s FYP was a belief held by students. This question (do you think the TikTok FYP is better tailored to your interests than other social media homepages) allowed students to select one of three options; yes, no or about the same.
I used the a four functions to manipulate my data frame; filter(), mutate(), groupby() and rename(). Once I’d manipulated my data, I plotted it using geom_jitter() and geom_boxplot() to create three boxplots grouped by opinion, recording the hours spent scrolling. I also ordered the opinions by the median scrolling time within each group (with most time at the top).
Breaking down plot 2!
Unsurprisingly, we can see that students who spent a high amount of time scrolling (over 2 hours per day) believe the TikTok FYP is better tailored to their interets than other social media homepages, with a far higher median.
There was no a large difference in scrolling time medians between students who believed the FYP was about the same or not better tailored than other social media hompages, though there were a couple of outliers. While students who felt the FYP was better tailored to their interests than other social media homepages had a more normal distribution, students who felt about the same or answered no had data that was more left skewed.
I would repeat this survey again with a larger sample size to see if this differentiates results between “no” opinions and “about the same” opinions!
Plot 3: Average hours students spent scrolling the TikTok FYP before and after April 2025, grouped by Age
I then decided to investigate if the average number of hours students spent scrolling TikTok’s FYP varied across different age groups, and if this behavior changed before and after April 2025 (using my Timestamp record).
I found this plot the toughest to plot, using mutate to convert the Timestamp column from a string into a date-time object, then dropping the “time” element so only the recorded date “day” was kept. Functions other than mutate that I used were rename(), group_by() and summarise(). I then used geom_line() and geom_point() to plot the data.
Investigating plot 3!
What we can see from this plot is that overall, the younger age groups / people under 30 spent a higher mean amount of hours scrolling the TikTok FYP in comparison to those above 30. In terms of whether students behaviour changed before and after 2025, it appears the younger students were still spending the highest mean amount of hours scrolling the FYP. 30-40 year old students showed a massive dropoff after April 2025 in mean hours scrolling and the reverse seen in the 25-30 student age group before April 2025.
What, if anything this tells us is the survey response date is unlikely to have had a massive impact on the mean scrolling hours of students when grouped by age. This is probably due to the small difference in dates and you may for example you might find some very different data comparing present results to the time where TikTok being banned was a large topic of discussion in early 2025.
However, my main takeaway from this was younger students have a higher mean hours spent scrolling TikTok than older students 30+
In Conclusion…
There is some very interesting data and trends revealed by my plots about the content students are recommended on their FYP and how this relates to their interactions, age, scrolling time etc! I would like to redo my survey sampling a wider group of students, possibly 6+ months apart in response time to see if these patterns change.
